Spatial modelling of malaria cases associated with environmental factors in South Sumatra, Indonesia

Abstract

Background: Malaria, a parasitic infection, is a life-threatening disease in South Sumatra Province, Indonesia. This study aimed to investigate the spatial association between malaria occurrence and environmental risk factors. Methods:The number of confirmed malaria cases was analysed for the year 2013 from the routine reporting of the Provincial Health Office of South Sumatra. The cases were spread over 436 out of 1613 villages. Six potential ecological predictors of malaria cases were analysed in the different regions using ordinary least square (OLS) and geographically weighted regression (GWR). The global pattern and spatial variability of associations between malaria cases and the selected potential ecological predictors was explored. Results:The importance of different environmental and geographic parameters for malaria was shown at global and village-level in South Sumatra, Indonesia. The independent variables altitude, distance from forest, and rainfall in global OLS were significantly associated with malaria cases. However, as shown by GWR model and in line with recent reviews, the relationship between malaria and environmental factors in South Sumatra strongly varied spatially in different regions. Conclusions:A more in-depth understanding of local ecological factors influencing malaria disease as shown in present study may not only be useful for developing sustainable regional malaria control programmes, but can also benefit malaria elimination efforts at village level.

Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon

“…For example, an increase in rainfall in a rainy season in Cameroon will promote malaria cases and these periods can be targeted for malaria prevention programs since rainfall creates breeding sites for female Anopheline mosquitoes. Similarly, a negative coefficient means X and Y changed in reverse directions (14). For example, the negative coefficient for aridity in our model means the malaria cases decreases with lack of water since aridity is a deficiency of moisture probably due to the lack of rainfall.…”

“…The local GWR model was built based on the variables from the global OSL model. A validated OLS can lead to a global policy for malaria control programs while a validated spatial relationship with GWR is an appropriate method to initiate prevention programs in local systems (14). The GWR output coefficients maps indicated that population density, EVI, rainfall, and drought episodes had a strong correlation or positive influence on malaria cases in our study locations.…”

“…The GWR adjusted R 2 value was higher (24.3 %) than the OLS model as expected to indicate improvement with local GWR model. This signifies that the GWR is a better indicator for explaining spatial variability at the local level (14,22). The map of the generated R 2 ( figure 5A) indicates that, the GWR explains 24.3% of the spatial variability between malaria cases and the environmental factors in the Northwest, Southwest, Littoral, Douala, Central and Yaoundé DHS regions and a weak or low spatial variability in some areas in the East, Adamawa and North regions.…”

“…This Statistic to determine overall model significance(49).The Jarque-Bera Statistics[g] was statistically significant (p < 0.01) indicating that our model predictions were biased (the residuals were not normally distributed); this may be due to the changing signs in some of the coefficients in the explanatory variable and thus causing variability. Though the test was biased, we proceeded to GWR model because recent studies (14) have reported on spatial variations similar to our specified model variables and our main goal was to understand the behavior of these variables with malaria cases for future research and intervention projects. The R-squared and Akaike's Information Criterion (AICc) measures the model fit/performance.…”

“…It is a local form of linear regression used to model spatially varying relationships. GWR statistical modelling technique has been applied to a range of malaria studies: Hasyim (14), used the GWR to find the spatial association between malaria cases and environmental factors in South Sumatra, Indonesia where altitude, distance from forest and rainfall was associated with malaria, Moise(15), in the seasonal and geographic variation of pediatric malaria in Burundi, identified the spatial variation between monthly rainfall and malaria prevalence. While the GWR spatial modelling technique has been a powerful tool in the understanding of spatial variability of malaria cases and environmental factors (rainfall, distance from forest, altitude, vegetation index, temperature, and proximity to water body), and other malaria prevention studies (14)(15)(16)(17), its application has been valuable in the understanding of other health outcomes and social science studies including cancer events (18), dengue fever (19), mental depression (20), fire events (21), hospital accessibility study (22), alcohol and violence(23) and real estate housing crisis (24).…”